ETC5512 Assignment 4: An Analysis of US Air Traffic

0.1 ✈️ Exercise 1
0.2 ✈️ Exercise 2
- 0.2.1 a.
- 0.2.2 b.
- 0.2.3 c.
- 0.2.4 d.
- 0.2.5 e.
0.3 ✈️ Exercise 3
0.4 ✈️ Exercise 4
0.5 Acknowledgments
References

0.1 ✈️ Exercise 1

0.1.1 a.

(#tab:load_ATL_feb_UA.rda)Carrier UA in ATL airport in Feb 2020
Year	Month	DayofMonth	Reporting_Airline	Origin	Dest
2020	2	29	UA	IAD	ATL
2020	2	29	UA	ATL	SFO
2020	2	29	UA	EWR	ATL
2020	2	29	UA	EWR	ATL
2020	2	29	UA	SFO	ATL
2020	2	29	UA	ORD	ATL

Here, I chose the UA airline’ flights related to ATL airport in Feb 2020.

First, I read the data from csv file. Then, I filter the data set and save the data set as rda file. Next, (in 1.b) I joined airport data set into the data set and save the new data set as rda file. Now, I can load the file to save directly.

0.1.2 b.

I join airport information data set into the data set created in last question. I used join function twice for “origin” and “dest” airports, respectively. At last, I saved the new data set as rda file.

0.1.3 c.

## Source : http://tile.stamen.com/terrain/4/2/5.png

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## Source : http://tile.stamen.com/terrain/4/2/6.png

## Source : http://tile.stamen.com/terrain/4/3/6.png

## Source : http://tile.stamen.com/terrain/4/4/6.png

## Source : http://tile.stamen.com/terrain/4/5/6.png

## Source : http://tile.stamen.com/terrain/4/2/7.png

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## Source : http://tile.stamen.com/terrain/4/4/7.png

## Source : http://tile.stamen.com/terrain/4/5/7.png

The level of transparency of the lines specifies how often does UA carry flights between ATL airport and other airports in Feb 2020. The deeper the color means more flights between the two airports.

0.2 ✈️ Exercise 2

0.2.1 a.

(#tab:standardise timezone)Flights-after normalise as NY Time
Origin_DateTime_NY	Dest_DateTime_NY	Tail_Number	Origin	Dest
2020-02-29 08:08:00	2020-02-29 09:44:00	N498UA	IAD	ATL
2020-02-29 07:11:00	2020-02-29 12:29:00	N853UA	ATL	SFO
2020-02-29 11:00:00	2020-02-29 13:07:00	N855UA	EWR	ATL
2020-02-29 14:10:00	2020-02-29 16:12:00	N15712	EWR	ATL
2020-02-29 03:38:00	2020-02-29 07:46:00	N812UA	SFO	ATL
2020-02-29 14:55:00	2020-02-29 16:38:00	N459UA	ORD	ATL

0.2.2 b.

## # A tibble: 211 x 2
## # Groups:   Tail_Number [132]
##    Tail_Number DayofMonth
##    <chr>            <dbl>
##  1 N12238              25
##  2 N14237              13
##  3 N15712              29
##  4 N16234               3
##  5 N16709              22
##  6 N16732              23
##  7 N18223               9
##  8 N27205              26
##  9 N27734              15
## 10 N33209               1
## # ... with 201 more rows

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0.2.3 c.

First, I created a function for drawing flight path of each plane. Then, I used to the function to create the table of flight path. Finally, I used the flight path table to draw the map. The colour represents the time.

Sometimes, a dot are far away from previous dot. This situation only happens near the destination airports, because the time interval is greater than 15 minutes, unlike other dots.

0.2.4 d.

Here, the gif specifies the flight path of this plane, showing its position every 15 minutes.

0.2.5 e.

0.3 ✈️ Exercise 3

0.4 ✈️ Exercise 4

This report is visible on my GitHub. The README.md file specifies the data sets in the data folder. It contains the variable explanations.

0.5 Acknowledgments

Data for Exercise 3:

flights data was downloaded from Bureau of Transportation Statistics
airports and airlines data were downloaded from Airport, airline and route data
planes data was downloaded from Federal Aviation Administration
weather data was downloaded from Iowa State University
city data was scraped from wikipedia - Atlanta

Thanks to Hadley Wickham, the owner and maker of nycflights13 packages. I made the rda files and README.md following the documentation of nycflights13.

Packages used to produce this assignment are:

tidyverse (Wickham et al. 2019), ggmap (Kahle and Wickham 2013), ggthemes (Arnold 2019), lutz (Teucher 2019), lubridate (Grolemund and Wickham 2011), kableExtra (Zhu 2019), janitor (Firke 2020), viridis (Garnier 2018), bookdown (Xie 2016)

References

Arnold, Jeffrey B. 2019. Ggthemes: Extra Themes, Scales and Geoms for ’Ggplot2’. https://CRAN.R-project.org/package=ggthemes.

Firke, Sam. 2020. Janitor: Simple Tools for Examining and Cleaning Dirty Data. https://CRAN.R-project.org/package=janitor.

Garnier, Simon. 2018. Viridis: Default Color Maps from ’Matplotlib’. https://CRAN.R-project.org/package=viridis.

Grolemund, Garrett, and Hadley Wickham. 2011. “Dates and Times Made Easy with lubridate.” Journal of Statistical Software 40 (3): 1–25. http://www.jstatsoft.org/v40/i03/.

Kahle, David, and Hadley Wickham. 2013. “Ggmap: Spatial Visualization with Ggplot2.” The R Journal 5 (1): 144–61. https://journal.r-project.org/archive/2013-1/kahle-wickham.pdf.

Teucher, Andy. 2019. Lutz: Look up Time Zones of Point Coordinates. https://CRAN.R-project.org/package=lutz.

Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy D’Agostino McGowan, Romain François, Garrett Grolemund, et al. 2019. “Welcome to the tidyverse.” Journal of Open Source Software 4 (43): 1686. https://doi.org/10.21105/joss.01686.

Xie, Yihui. 2016. Bookdown: Authoring Books and Technical Documents with R Markdown. Boca Raton, Florida: Chapman; Hall/CRC. https://github.com/rstudio/bookdown.

Zhu, Hao. 2019. KableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax. https://CRAN.R-project.org/package=kableExtra.